The Glorot normal initializer, also called Xavier normal initializer.
Inherits From: VarianceScaling
tf.compat.v1.keras.initializers.glorot_normal(
seed=None,
dtype=tf.dtypes.float32
)
It draws samples from a truncated normal distribution centered on 0
with standard deviation (after truncation) given by
stddev = sqrt(2 / (fan_in + fan_out))
where fan_in
is the number
of input units in the weight tensor and fan_out
is the number of
output units in the weight tensor.
Args |
seed
|
A Python integer. Used to create random seeds. See
tf.compat.v1.set_random_seed for behavior.
|
dtype
|
Default data type, used if no dtype argument is provided when
calling the initializer. Only floating point types are supported.
|
Methods
from_config
View source
@classmethod
from_config(
config
)
Instantiates an initializer from a configuration dictionary.
Example:
initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)
Args |
config
|
A Python dictionary. It will typically be the output of
get_config .
|
Returns |
An Initializer instance.
|
get_config
View source
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
Returns |
A JSON-serializable Python dict.
|
__call__
View source
__call__(
shape, dtype=None, partition_info=None
)
Returns a tensor object initialized as specified by the initializer.
Args |
shape
|
Shape of the tensor.
|
dtype
|
Optional dtype of the tensor. If not provided use the initializer
dtype.
|
partition_info
|
Optional information about the possible partitioning of a
tensor.
|